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- Title
Temperature prediction model for a high-speedmotorized spindle based on back-propagation neural networkoptimized by adaptive particle swarm optimization.
- Authors
Lei Chunli; Zhao Mingqi; Liu Kai; Song Ruizhe; Zhang Huqiang
- Abstract
To predict the temperature of a motorized spindle more accurately, a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed. First, on the basis of the PSO-BPNN algorithm, the adaptive inertia weight is introduced to make the weight change with the fitness of the particle, the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm, the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence, and the APSO-BPNN model is constructed. Then, the temperature of different measurement points of the motorized spindle is forecasted by the BPNN, PSO-BPNN, and APSO-BPNN models. The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness. The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.
- Subjects
NEURAL circuitry; ALGORITHMS; TEMPERATURE; MACHINE tools; TECHNOLOGY convergence
- Publication
Journal of Southeast University (English Edition), 2022, Vol 38, Issue 3, p235
- ISSN
1003-7985
- Publication type
Article
- DOI
10.3969/j.issn.1003-7985.2022.03.004